Pending Review Microbiomes contribute to multiple ecosystem services by transforming organic matter in soil. Extreme shifts in the environment, such as drying-rewetting cycles during drought, can impact microbial metabolism of organic matter by altering their physiology and function. These...
Filter results
Content type
Tags
- (-) Mass spectrometry-based Omics (3)
- Omics (10)
- PerCon SFA (9)
- High Throughput Sequencing (8)
- Genomics (7)
- Machine Learning (6)
- Type 1 Diabetes (6)
- Autoimmunity (5)
- Sequencer System (5)
- Synthetic Biology (5)
- Biomarkers (4)
- Mass Spectrometry (4)
- Molecular Profiling (4)
- RNA Sequence Analysis (4)
- Predictive Modeling (3)
- Software Data Analysis (3)
- Statistical Expression Analysis (3)
- Amplicon Sequencing (2)
- Biological and Environmental Research (2)
- DNA Sequence Analysis (2)
- Functional Annotation Analysis (2)
- Imaging (2)
- Kmers (2)
- Long Read Sequencer (2)
- Mass spectrometry data (2)
- Proteomics (2)
- Python (2)
- Snakemake (2)
- Spectroscopy (2)
- Whole Genome Sequencing (2)
Fusarium sp. DS682 Proteogenomics Statistical Data Analysis of SFA dataset download: 10.25584/KSOmicsFspDS682/1766303 . GitHub Repository Source: https://github.com/lmbramer/Fusarium-sp.-DS-682-Proteogenomics MaxQuant Export Files (txt) Trelliscope Boxplots (jsonp) Fusarium Report (.Rmd, html)...
Machine learning is a core technology that is rapidly advancing within type 1 diabetes (T1D) research. Our Human Islet Research Network (HIRN) grant is studying early cellular response initiating β cell stress in T1D through the generation of heterogenous low- and high-throughput molecular...
Datasets
3
The Environmental Determinants of Diabetes in the Young (TEDDY) study is searching for factors influencing the development of type 1 diabetes (T1D) in children. Research has shown that there are certain genes that correlate to higher risk of developing T1D, but not all children with these genes...
Datasets
1
The Diabetes Autoimmunity Study in the Young (DAISY) seeks to find environmental factors that can trigger the development of type 1 diabetes (T1D) in children. DAISY follows children with high-risk of developing T1D based on family history or genetic markers. Genes, diets, infections, and...
Datasets
1